The capacity of various types of IMDs to collect and store large amounts of physiological data is increasing. Some IMDs implement pre-programmed algorithms to process collected physiological data to, for example, diagnose certain patient conditions and/or guide therapy delivered by the IMD. Some IMDs, such as implantable pacemaker-cardioverter-defibrillators, may implement algorithms to detect and classify cardiac arrhythmia episodes, in response to which the IMD may deliver therapy, such as a defibrillation shock.
IMDs may also implement one or more algorithms to monitor device integrity by, for example, tracking events characteristic of physiological sensing issues and/or device impedance changes. Certain components of implantable medical devices, like any other man-made device, are subject to fault or failure, for example, either due to operator error at the time of implant, or due to normal “wear and tear” on the components during the life of the device. These faults or failures can result in artifact signals which are sensed by the IMD and mistaken for physiological signals, thereby impacting the accuracy of the data analysis performed by the IMD in classifying episodes.
Many sophisticated data processing algorithms have been developed to perform more detailed analyses of data collected by IMDs. Some of these algorithms have been programmed on an external medical device that can be wirelessly coupled, for example, via telemetry, to an IMD for the transfer of the data from the IMD to the external device. Alternately, some of the algorithms have been programmed on computers, which are not wirelessly coupled to the device, and the data is transferred from external medical devices to the computers, for example personal or lap top computers, on a disk or via a network.
Such external algorithms can process the data received from IMDs to come to some conclusions regarding episode classification and events indicative of device integrity issues, and may further provide a presentation of the data in a format that allows a physician or clinician to further analyze the data. Such auxiliary analyses of data transferred from an IMD can allow a clinician to monitor patient condition, bring to light device integrity issues, and/or errors in one or more analyses performed by the IMD that have led to misclassification of episodes detected by the IMD. Such auxiliary analysis may thus help an attending physician or other clinician in making decisions to reposition or replace certain portions/components of the device due to faults or failures detected by the auxiliary analysis, and/or in making decisions related to re-programming of the implanted device in order to prevent misclassification errors in the future.
Methods employed by an algorithm for post processing of data associated with arrhythmic episodes, which are detected and classified by an implantable cardioverter defibrillator (ICD), are described in commonly assigned U.S. Pat. No. 7,894,883, entitled METHOD AND APPARATUS FOR POST-PROCESSING OF EPISODE DETECTED BY A MEDICAL DEVICE, incorporated herein by reference in its entirety. Examples of methods employed by algorithms that are tailored to identify and classify events indicative of a device integrity issue, in particular, faults or failures associated with lead components of the device, are described in commonly assigned U.S. Pat. No. 7,539,540, entitled TROUBLESHOOTING METHODS FOR A MEDICAL SYSTEM INCLUDING IMPLANTABLE COMPONENTS and in U.S. Pat. No. 7,047,083, entitled METHOD AND APPARATUS FOR IDENTIFYING LEAD-RELATED CONDITIONS USING LEAD IMPEDANCE MEASUREMENTS, which are each hereby incorporated by reference in their entireties.